1. Field of the Invention
This invention relates generally to linguistic retrieval systems and more specifically to word prediction systems.
2. Description of Related Art
Known word prediction systems operate based solely upon input letter activations. As each letter or group of letters is input to the system, a list of words is offered to the operator for selection. The list of words offered to the operator may be ordered by a designated frequency of use, by a general alphabetic order, or by a combination thereof. The list of displayed words can be modified by entering a next letter of the desired word. As such, a desired word (if stored in the word prediction database) is eventually displayed as a word choice; but it may take the entry of several letters of the desired word before it eventually appears on the list (or the typing of the entire word, if it is not stored in the word prediction database).
As an example, if the user desires to output the word “apple”, the user first inputs an “a”. The system then gathers and displays a predetermined number of words (for example, six), beginning with the letter “a”. These six displayed words may be the first six words alphabetically stored under “a” in a database including “aardvark”; “aback”; “abacus”; “abaft”; “abalone” and “abandon”, for example; or may include frequently used words beginning with the word “a”. Thus, unless “apple” is a frequently used word which automatically appears as a selection upon inputting the letter “a”, it may take the entry of several letters in the word before the word “apple” appears as one of the six words for selection. A user might have to enter “app”, before the word “apple” appears as a selection and thus it will require at least four hits (three hits for each of “a”, “p”, and “p”, and one additional hit to select the displayed word “apple”) for someone to select the word “apple” using a known word prediction system.
The problem with known word prediction systems is that they require many keystrokes to access a word. Speech synthesis systems which utilize word prediction, for example, need to minimize keystroke activation necessary to access a word, as the people using such systems may be, and typically are, cognitively impaired in some way. It is difficult for people with diseases such as ALS, commonly known as Lou Gerrig's disease, to make these selections and it is further difficult for such people to retain the level of concentration necessary to remember the word which he or she is trying to obtain through word prediction, let alone to remember the context in which the word is to be used in a sentence or conversation. Thus, a need exists for the creation of a simple word prediction system which minimizes the number of keystrokes necessary to access a word.
Highly agglutinating and inflection languages pose an even greater problem to existing word prediction systems. A highly agglutinating language such as the German language, for example, is one in which words are combined fairly freely into larger words, and one which makes use of agglutination as its most productive strategy of lexical expansion. Since highly agglutinating languages contain large sets of words all beginning with the same letters and, more importantly, the same groups of letters, the reduction of a list of words offered or predicted to an operator to a manageable set becomes nearly impossible using known word prediction methods. In addition, multiple suffix inflections on individual words can cause a similar difficulty to existing word prediction systems in reducing the number of words offered to the operator after a base word has been chosen.
Although known word prediction systems for accessing words stored in a database allows a considerable enhancement over straight spelling, highly agglutinating languages such as German cannot be efficiently handled with such current word prediction systems. Such word prediction systems for highly agglutinating language would contain a large number of entries all beginning with the same long sequence of letters. For example, in a sample database, 198 words beginning with “zeit” were counted. In such a situation, “z”, “e”, “i”, and “t”, have to be entered to even begin narrowing down the list of choices to reach a desired word such as “Zeitungskiosk” (meaning “newspaper stand”). For example, even after a fifth letter “u” is typed (thus, after five key activations), the following traces remain:
zeitung
zeitungen
zeitungsanzeige
zeitungsanzeigen
zeitungsausschnitt
zeitungsausschnitts
zeitungsausscbnitten
zeitungshandler
zeitungshandlers
zeitungsjunge
zeitungsjungen
zeitungskiosk
zeitungskioske
zeitungskiosks
zeitungskiosken
zeitungspapier
zeitungspapiere
zeitungspapiers
zeitungspapieren
To continue to narrow down the choices, the letters “n”, “g”, “s”, and “k” (nine key activations) must be typed before the display will reach the following four choices:
zeitungskiosk
zeitungskioske
zeitungskiosks
zeitungskiosken
Finally, from the four words listed above, the word “zeitungskiosk” can finally be selected. Thus, ten key activations are necessary to access such a word utilizing known word prediction systems in a highly agglutinating language such as German. Accordingly, a need exists for enhancing current word prediction systems, especially when used in highly agglutinating languages such as German.